From the 1 of 5 linked papers with an AI index.
5 papers
Leveraging unlabelled data for generalizable neural population decoding
Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo +2
The paper presents MOJO, a framework that combines masked autoencoding self‑supervised learning with supervised training for spike‑tokenizing neural decoders, yielding better decod…
JEDI: Jointly Embedded Inference of Neural Dynamics
Anirudh Jamkhandi, Ali Korojy, Olivier Codol +2
Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to map the mechanisms of the brain's flexib…
Generalizable, real-time neural decoding with hybrid state-space models
Avery Hee-Woon Ryoo, Nanda H. Krishna, Ximeng Mao +4
Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces, where models are subje…
POCO: Scalable Neural Forecasting through Population Conditioning
Yu Duan, Hamza Tahir Chaudhry, Misha B. Ahrens +4
Predicting future neural activity is a core challenge in modeling brain dynamics, with applications ranging from scientific investigation to closed-loop neurotechnology. While rece…
Expressivity of Neural Networks with Random Weights and Learned Biases
Ezekiel Williams, Alexandre Payeur, Avery Hee-Woon Ryoo +4
Landmark universal function approximation results for neural networks with trained weights and biases provided the impetus for the ubiquitous use of neural networks as learning mod…